Statistical Shape Modelling and Segmentation of the Respiratory Airway

The human respiratory airway consists of the upper (nasal cavity, pharynx) and the lower (trachea, bronchi) respiratory tracts. Accurate segmentation of these two airway tracts can lead to better diagnosis and interpretation of airway-specific diseases, and lead to improvement in the localization of abnormal metabolic or pathological sites found within and/or surrounding the respiratory regions. Due to the complexity and the variability displayed in the anatomical structure of the upper respiratory airway along with the challenges in distinguishing the nasal cavity from non-respiratory regions such as the paranasal sinuses, it is difficult for existing algorithms to accurately segment the upper airway without manual intervention. This thesis presents an implicit non-parametric framework for constructing a statistical shape model (SSM) of the upper and lower respiratory tract, capable of distinct shape generation and be adapted for segmentation. An SSM of the nasal cavity was successfully constructed using 50 nasal CT scans. The performance of the SSM was evaluated for compactness, specificity and generality. An averaged distance error of 1.47 mm was measured for the generality assessment. The constructed SSM was further adapted with a modified locally constrained random walk algorithm to segment the nasal cavity. The proposed algorithm was evaluated on 30 CT images and outperformed comparative state-of-the-art and conventional algorithms. For the lower airway, a separate algorithm was proposed to automatically segment the trachea and bronchi, and was designed to tolerate the image characteristics inherent in low-contrast CT images. The algorithm was evaluated on 20 clinical low-contrast CT from PET-CT patient studies and demonstrated better performance (87.1±2.8 DSC and distance error of 0.37±0.08 mm) in segmentation results against comparative state-of-the-art algorithms

Patient-specific simulation environment for surgical planning and preoperative rehearsal

Surgical simulation is common practice in the fields of surgical education and training. Numerous surgical simulators are available from commercial and academic organisations for the generic modelling of surgical tasks. However, a simulation platform is still yet to be found that fulfils the key requirements expected for patient-specific surgical simulation of soft tissue, with an effective translation into clinical practice. Patient-specific modelling is possible, but to date has been time-consuming, and consequently costly, because data preparation can be technically demanding. This motivated the research developed herein, which addresses the main challenges of biomechanical modelling for patient-specific surgical simulation. A novel implementation of soft tissue deformation and estimation of the patient-specific intraoperative environment is achieved using a position-based dynamics approach. This modelling approach overcomes the limitations derived from traditional physically-based approaches, by providing a simulation for patient-specific models with visual and physical accuracy, stability and real-time interaction. As a geometrically- based method, a calibration of the simulation parameters is performed and the simulation framework is successfully validated through experimental studies. The capabilities of the simulation platform are demonstrated by the integration of different surgical planning applications that are found relevant in the context of kidney cancer surgery. The simulation of pneumoperitoneum facilitates trocar placement planning and intraoperative surgical navigation. The implementation of deformable ultrasound simulation can assist surgeons in improving their scanning technique and definition of an optimal procedural strategy. Furthermore, the simulation framework has the potential to support the development and assessment of hypotheses that cannot be tested in vivo. Specifically, the evaluation of feedback modalities, as a response to user-model interaction, demonstrates improved performance and justifies the need to integrate a feedback framework in the robot-assisted surgical setting.Open Acces

Automatic 3D Cranial Landmark Positioning based on Surface Curvature Feature using Machine Learning

Cranial anthropometric reference points (landmarks) play an important role in craniofacial reconstruction and identification. Knowledge to detect the position of landmarks is critical. This work aims to locate landmarks automatically. Landmarks positioning using Surface Curvature Feature (SCF) is inspired by conventional methods of finding landmarks based on morphometrical features. Each cranial landmark has a unique shape. With the appropriate 3D descriptors, the computer can draw associations between shapes and landmarks using machine learning. The challenge in classification and detection in three-dimensional space is to determine the model and data representation. Using three-dimensional raw data in machine learning is a serious volumetric issue. This work uses the Surface Curvature Feature as a three-dimensional descriptor. It extracts the local surface curvature shape into a projection sequential value (depth). A machine learning method is developed to determine the position of landmarks based on local surface shape characteristics. Classification is carried out from the top-n prediction probabilities for each landmark class, from a set of predictions, then filtered to get pinpoint accuracy. The landmark prediction points are hypothetically clustered in a particular area, so a cluster-based filter is appropriate to isolate them. The learning model successfully detected the landmarks, with the average distance between the prediction points and the ground truth being 0.0326 normalized units. The cluster-based filter is implemented to increase accuracy compared to the ground truth. Thus, SCF is suitable as a 3D descriptor of cranial landmarks

Computer Muscle Modelling

This work was supported by the projects SGS-2019-016 of the Czech Ministry of Education, GA 17-05534S of the Czech Science Foundation and partially by the project IDEG-2021-027 of the West Bohemia University.Nowadays, computer muscle modelling plays a more and more important role in the threatment of various musculoskeletal diseases. For example, to find the appropriate artificial joint replacement, physicians should know the anatomy and dynamic properties of the specific patient. Various models and algorithms help physicians. In this report, state-of-the-art methods are discussed. Then, a competitive modified position-based dynamics approach is presented. The speed/quality ratio of the proposed method is adjustable and achieves certain results over a specified period

A Framework for the Semantics-aware Modelling of Objects

The evolution of 3D visual content calls for innovative methods for modelling shapes based on their intended usage, function and role in a complex scenario. Even if different attempts have been done in this direction, shape modelling still mainly focuses on geometry. However, 3D models have a structure, given by the arrangement of salient parts, and shape and structure are deeply related to semantics and functionality. Changing geometry without semantic clues may invalidate such functionalities or the meaning of objects or their parts. We approach the problem by considering semantics as the formalised knowledge related to a category of objects; the geometry can vary provided that the semantics is preserved. We represent the semantics and the variable geometry of a class of shapes through the parametric template: an annotated 3D model whose geometry can be deformed provided that some semantic constraints remain satisfied. In this work, we design and develop a framework for the semantics-aware modelling of shapes, offering the user a single application environment where the whole workflow of defining the parametric template and applying semantics-aware deformations can take place. In particular, the system provides tools for the selection and annotation of geometry based on a formalised contextual knowledge; shape analysis methods to derive new knowledge implicitly encoded in the geometry, and possibly enrich the given semantics; a set of constraints that the user can apply to salient parts and a deformation operation that takes into account the semantic constraints and provides an optimal solution. The framework is modular so that new tools can be continuously added. While producing some innovative results in specific areas, the goal of this work is the development of a comprehensive framework combining state of the art techniques and new algorithms, thus enabling the user to conceptualise her/his knowledge and model geometric shapes. The original contributions regard the formalisation of the concept of annotation, with attached properties, and of the relations between significant parts of objects; a new technique for guaranteeing the persistence of annotations after significant changes in shape's resolution; the exploitation of shape descriptors for the extraction of quantitative information and the assessment of shape variability within a class; and the extension of the popular cage-based deformation techniques to include constraints on the allowed displacement of vertices. In this thesis, we report the design and development of the framework as well as results in two application scenarios, namely product design and archaeological reconstruction

Registration of ultrasound and computed tomography for guidance of laparoscopic liver surgery

Laparoscopic Ultrasound (LUS) imaging is a standard tool used for image-guidance during laparoscopic liver resection, as it provides real-time information on the internal structure of the liver. However, LUS probes are di cult to handle and their resulting images hard to interpret. Additionally, some anatomical targets such as tumours are not always visible, making the LUS guidance less e ective. To solve this problem, registration between the LUS images and a pre-operative Computed Tomography (CT) scan using information from blood vessels has been previously proposed. By merging these two modalities, the relative position between the LUS images and the anatomy of CT is obtained and both can be used to guide the surgeon. The problem of LUS to CT registration is specially challenging, as besides being a multi-modal registration, the eld of view of LUS is signi cantly smaller than that of CT. Therefore, this problem becomes poorly constrained and typically an accurate initialisation is needed. Also, the liver is highly deformed during laparoscopy, complicating the problem further. So far, the methods presented in the literature are not clinically feasible as they depend on manually set correspondences between both images. In this thesis, a solution for this registration problem that may be more transferable to the clinic is proposed. Firstly, traditional registration approaches comprised of manual initialisation and optimisation of a cost function are studied. Secondly, it is demonstrated that a globally optimal registration without a manual initialisation is possible. Finally, a new globally optimal solution that does not require commonly used tracking technologies is proposed and validated. The resulting approach provides clinical value as it does not require manual interaction in the operating room or tracking devices. Furthermore, the proposed method could potentially be applied to other image-guidance problems that require registration between ultrasound and a pre-operative scan

Visualisation of articular motion in orthopaedics

Shouder replacement surgery is difficult surgery, with a relatively large risk on limited post-operative range of motion for patients. Adaptations to the anatomy of joints by placing a prosthesis affects the articulation of the joint. In this thesis we present a software system that simulates and visualises these effects. By loading a CT-scan of the shoulder of a patient we can simulate the range of motion of the joint and visualize limitations as a result of rigid structures of the joint. Surgeons may set up an operation plan and see what the consequences of the operation will be for the range of motion of the patient. The thesis investigates aspects that are relevant for the system. We describe an algorithm to convert the scan data to bone models. In addition, a validation experiment is presented. A method for motion registration and visualisation of recorded kinematic data is presented. Finally, this thesis concerns the application of the system to different surgical problems, such as hip arthroplasty and shoulder fractures.Annafonds Biomet Nederland Clinical Graphics DePuy JTE Johnson & Johnson Dutch Arthritis Association Litos/ Motek Medical TornierUBL - phd migration 201

Coronary Artery Segmentation and Motion Modelling

Conventional coronary artery bypass surgery requires invasive sternotomy and the use of a cardiopulmonary bypass, which leads to long recovery period and has high infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery based on image guided robotic surgical approaches have been developed to allow the clinicians to conduct the bypass surgery off-pump with only three pin holes incisions in the chest cavity, through which two robotic arms and one stereo endoscopic camera are inserted. However, the restricted field of view of the stereo endoscopic images leads to possible vessel misidentification and coronary artery mis-localization. This results in 20-30% conversion rates from TECAB surgery to the conventional approach. We have constructed patient-specific 3D + time coronary artery and left ventricle motion models from preoperative 4D Computed Tomography Angiography (CTA) scans. Through temporally and spatially aligning this model with the intraoperative endoscopic views of the patient's beating heart, this work assists the surgeon to identify and locate the correct coronaries during the TECAB precedures. Thus this work has the prospect of reducing the conversion rate from TECAB to conventional coronary bypass procedures. This thesis mainly focus on designing segmentation and motion tracking methods of the coronary arteries in order to build pre-operative patient-specific motion models. Various vessel centreline extraction and lumen segmentation algorithms are presented, including intensity based approaches, geometric model matching method and morphology-based method. A probabilistic atlas of the coronary arteries is formed from a group of subjects to facilitate the vascular segmentation and registration procedures. Non-rigid registration framework based on a free-form deformation model and multi-level multi-channel large deformation diffeomorphic metric mapping are proposed to track the coronary motion. The methods are applied to 4D CTA images acquired from various groups of patients and quantitatively evaluated